Detection of Groups in Non-Structured Data

Paradis, Rosemary D.; Davenport, Daniel L.; Menaker, David; Taylor, Sarah

doi:10.1016/j.procs.2012.09.095

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Words with high TF-IDF values are attributed a strong relationship with the document in which they appear. This, in turn, suggests that if that word appeared in a query, the document could be of interest to the user [14]. This can be expressed formally in the following manner: a set of documents D with the user entering a query q = { w 1 , w 2 , w 3 ...w n } for a sequence of words w i .Then, we return a subset D * of D such that for each d ∈ D * , the following probability is maximized: P(d | q, D) .…”

Section: Sociolect Identificationmentioning

confidence: 99%

“…Furthermore, sociolinguistics describes a sociolect as involving the use of vocabulary with special phonetic (accents) or syntactic characteristics, which are developed within group language according to the frequency of their interactions. In addition, sociolinguists focus on finding distinct vocabulary features and reconstructing the linguistic image of the world contained in the specific terminology of a group, based on a wide range of lexical and grammatical characteristics [12][13][14]. The latest trends in semi-supervised learning for social data analysis are expressed by studies embedding finite and infinite communities on graphs [15].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

et al. 2021

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The emergence of digital social networks has transformed society, social groups, and institutions in terms of the communication and expression of their opinions. Determining how language variations allow the detection of communities, together with the relevance of specific vocabulary (proposed by the National Council of Accreditation of Colombia (Consejo Nacional de Acreditación -CNA) to determine the quality evaluation parameters for universities in Colombia) in digital assemblages could lead to a better understanding of their dynamics and social foundations, thus resulting in better communication policies and intervention where necessary. The approach presented in this paper intends to determine what are the semantic spaces (sociolinguistic features) shared by social groups in digital social networks. It includes five layers based on Design Science Research, which are integrated with Natural Language Processing techniques (NLP), Computational Linguistics (CL), and Artificial Intelligence (AI). The approach is validated through a case study wherein the semantic values of a series of "Twitter" institutional accounts belonging to Colombian Universities are analyzed in terms of the 12 quality factors established by CNA. In addition, the topics and the sociolect used by different actors in the university communities are also analyzed. The current approach allows determining the sociolinguistic features of social groups in digital social networks. Its application allows detecting the words or concepts to which each actor of a social group (university) gives more importance in terms of vocabulary.

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Section: Sociolect Identificationmentioning

confidence: 99%